The diversity of communities of microorganisms (microbiota) in our bodies, in soils, and throughout all ecosystems known on Earth has only recently been realized. Different microbiota are found at multiple body sites - where some provide health benefits and others cause disease. Analysis of these communities will reveal new ways to determine predisposition to diseases and will enable manipulation of the human microbiota to optimize human health. Other microbiota are found in and around plant cells, where some provide nutritional and anti-disease benefits, while others cause disease. Understanding plant microbiota will identify and define new ecologically compatible and sustainable agricultural practices and enable agricultural expansion to currently unsuitable land. Furthermore, the unprecedented diversity of microorganisms throughout all ecosystems provides for tremendous and uncharted genetic diversity, with far-reaching industrial applications. Technology Hurdle: Much of the information we have about microbiota derives from high-throughput sequencing technologies. Because the vast majority of microorganisms are unknown and cannot be purified, the microorganisms in microbiota must remain mixed and are co-sequenced (metagenomics). Using conventional methods, it is difficult to decipher which sequences belong to specific microorganisms (deconvolution) because information regarding the cell of origin is lost upon breaking cells and preparation of DNA for sequencing - especially for complex genomes with multiple chromosomes or plasmids. Our Technological Advance: We used chromosome conformation capture, to make both intra- and inter-chromosomal DNA crosslinks (stable linkages) - prior to breaking cells and processing of DNA. This allowed us to know which sequences originated in the same cell during deconvolution. In our initial studies, we successfully assembled artificially mixed populations of microorganisms (fungal, bacterial, and archaeal species), including those with complex genomes. Hypothesis: We hypothesize that our method can be applied to natural microbiota with unknown species at unknown concentrations. Specific Aims: 1) To adapt wet-lab chromosome conformation capture methods to real-world metagenomic samples, including difficult and low-biomass samples; and 2) develop production-quality software optimized for assembling low-abundance genomes, strain deconvolution, and plasmid assignment. Overall Impact: Upon completion, we believe our methods will become the standard for metagenomic sequencing - to better realize the potential that discoveries of human, plant, and ecosystem microbiota have to offer.